论文标题
粒子群元启发术，用于实施不确定性，可鲁棒优化
Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty
论文作者
论文摘要
我们考虑不确定性下的全球非凸优化问题。在这种情况下，不可能准确地实现所需的解决方案。相反，可以实现与预期解决方案的一定距离内的任何其他解决方案。目的是找到一个强大的解决方案，即，在附近最坏的解决方案仍在尽可能及时执行的解决方案。 这种类型的问题展示了另一个最大化层，可以在找到强大的解决方案的最小化水平中找到最坏的案例解决方案，这使得与经典的全局优化问题更难解决。到目前为止，只有很少的方法可以应用于实现不确定性的黑框问题。我们通过引入一种基于粒子群的新型框架来改进现有技术，该框架适应了先前方法的元素，并将其与新功能结合在一起，以生成更有效的技术。在计算实验中，我们发现在近80％的病例中，我们的新方法优于最先进的比较器启发式方法。
We consider global non-convex optimisation problems under uncertainty. In this setting, it is not possible to implement a desired solution exactly. Instead, any other solution within some distance to the intended solution may be implemented. The aim is to find a robust solution, i.e., one where the worst possible solution nearby still performs as well as possible. Problems of this type exhibit another maximisation layer to find the worst case solution within the minimisation level of finding a robust solution, which makes them harder to solve than classic global optimisation problems. So far, only few methods have been provided that can be applied to black-box problems with implementation uncertainty. We improve upon existing techniques by introducing a novel particle swarm based framework which adapts elements of previous approaches, combining them with new features in order to generate more effective techniques. In computational experiments, we find that our new method outperforms state of the art comparator heuristics in almost 80% of cases.
